OpenRaiser/Intern-Atlas huggingface dataset:
Pursuing More Effective Graph Spectral Sparsifiers via Approximate Trace Reduction
Venue: arXiv cs.DS 2022
Year: 2022

Spectral graph sparsification aims to find ultra-sparse subgraphs which can preserve spectral properties of original graphs. In this paper, a new spectral criticality metric based on trace reduction is first introduced for identifying spectrally important off-subgraph edges. Then, a physics-inspired truncation strategy and an approach using approximate inverse of Cholesky factor are proposed to compute the approximate trace reduction efficiently. Combining them with the iterative densification scheme in \cite{feng2019grass} and the strategy of excluding spectrally similar off-subgraph edges in \cite{fegrass}, we develop a highly effective graph sparsification algorithm. The proposed method has been validated with various kinds of graphs. Experimental results show that it always produces sparsifiers with remarkably better quality than the state-of-the-art GRASS \cite{feng2019grass} in same computational cost, enabling more than 40% time reduction for preconditioned iterative equation solver on average. In the applications of power grid transient analysis and spectral graph partitioning, the derived iterative solver shows 3.3X or more advantages on runtime and memory cost, over the approach based on direct sparse solver.
Optimal routing algorithm for trips involving thousands of ev-charging stations using Kinetica-Graph
Venue: arXiv cs.DC 2022
Year: 2022

This paper discusses a graph based route solving algorithm to find the optimal path for an electric vehicle picking the best charging locations among thousands to minimize the total cumulative driving distance between the end points of the trip. To this end, we have devised a combinatorial optimization algorithm and a fixed storage graph topology construction for the graph road network of the continental USA. We have also re-purposed our existing Dijkstra solver to reduce the computational cost of many shortest path solves involved in the algorithm. An adaptive and light weight spatial search structure is also devised for finding a set of prospective stations at each charging location using uniform bins and double link associations. The entire algorithm is implemented as yet another multi-threaded at-scale graph solver within the suite of Kinetica-Graph analytics, exposed as a restful API endpoint and operable within SQL. Several example trips are solved and the results are demonstrated within the context.
A Modular Continuum Manipulator for Aerial Manipulation and Perching
Venue: arXiv cs.RO 2022
Year: 2022

Most aerial manipulators use serial rigid-link designs, which results in large forces when initiating contacts during manipulation and could cause flight stability difficulty. This limitation could potentially be improved by the compliance of continuum manipulators. To achieve this goal, we present the novel design of a compact, lightweight, and modular cable-driven continuum manipulator for aerial drones. We then derive a complete modeling framework for its kinematics, statics, and stiffness (compliance). The framework is essential for integrating the manipulator to aerial drones. Finally, we report preliminary experimental validations of the hardware prototype, providing insights on its manipulation feasibility. Future work includes the integration and test of the proposed continuum manipulator with aerial drones.
Transformer Lesion Tracker
Venue: arXiv cs.CV 2022
Year: 2022

Evaluating lesion progression and treatment response via longitudinal lesion tracking plays a critical role in clinical practice. Automated approaches for this task are motivated by prohibitive labor costs and time consumption when lesion matching is done manually. Previous methods typically lack the integration of local and global information. In this work, we propose a transformer-based approach, termed Transformer Lesion Tracker (TLT). Specifically, we design a Cross Attention-based Transformer (CAT) to capture and combine both global and local information to enhance feature extraction. We also develop a Registration-based Anatomical Attention Module (RAAM) to introduce anatomical information to CAT so that it can focus on useful feature knowledge. A Sparse Selection Strategy (SSS) is presented for selecting features and reducing memory footprint in Transformer training. In addition, we use a global regression to further improve model performance. We conduct experiments on a public dataset to show the superiority of our method and find that our model performance has improved the average Euclidean center error by at least 14.3% (6mm vs. 7mm) compared with the state-of-the-art (SOTA). Code is available at https://github.com/TangWen920812/TLT.
Energy Consumption Analysis of pruned Semantic Segmentation Networks on an Embedded GPU
Venue: arXiv cs.NE 2022
Year: 2022

Deep neural networks are the state of the art in many computer vision tasks. Their deployment in the context of autonomous vehicles is of particular interest, since their limitations in terms of energy consumption prohibit the use of very large networks, that typically reach the best performance. A common method to reduce the complexity of these architectures, without sacrificing accuracy, is to rely on pruning, in which the least important portions are eliminated. There is a large literature on the subject, but interestingly few works have measured the actual impact of pruning on energy. In this work, we are interested in measuring it in the specific context of semantic segmentation for autonomous driving, using the Cityscapes dataset. To this end, we analyze the impact of recently proposed structured pruning methods when trained architectures are deployed on a Jetson Xavier embedded GPU.
On the reusability of samples in active learning
Venue: arXiv cs.LG 2022
Year: 2022

An interesting but not extensively studied question in active learning is that of sample reusability: to what extent can samples selected for one learner be reused by another? This paper explains why sample reusability is of practical interest, why reusability can be a problem, how reusability could be improved by importance-weighted active learning, and which obstacles to universal reusability remain. With theoretical arguments and practical demonstrations, this paper argues that universal reusability is impossible. Because every active learning strategy must undersample some areas of the sample space, learners that depend on the samples in those areas will learn more from a random sample selection. This paper describes several experiments with importance-weighted active learning that show the impact of the reusability problem in practice. The experiments confirmed that universal reusability does not exist, although in some cases -- on some datasets and with some pairs of classifiers -- there is sample reusability. Finally, this paper explores the conditions that could guarantee the reusability between two classifiers.
Learning Domain Adaptive Object Detection with Probabilistic Teacher
Venue: arXiv cs.CV 2022
Year: 2022

Self-training for unsupervised domain adaptive object detection is a challenging task, of which the performance depends heavily on the quality of pseudo boxes. Despite the promising results, prior works have largely overlooked the uncertainty of pseudo boxes during self-training. In this paper, we present a simple yet effective framework, termed as Probabilistic Teacher (PT), which aims to capture the uncertainty of unlabeled target data from a gradually evolving teacher and guides the learning of a student in a mutually beneficial manner. Specifically, we propose to leverage the uncertainty-guided consistency training to promote classification adaptation and localization adaptation, rather than filtering pseudo boxes via an elaborate confidence threshold. In addition, we conduct anchor adaptation in parallel with localization adaptation, since anchor can be regarded as a learnable parameter. Together with this framework, we also present a novel Entropy Focal Loss (EFL) to further facilitate the uncertainty-guided self-training. Equipped with EFL, PT outperforms all previous baselines by a large margin and achieve new state-of-the-arts.
Internet of Things (IoT) based Smart Agriculture Aiming to Achieve Sustainable Goals
Venue: arXiv cs.CY 2022
Year: 2022

Despite the fact, a handful of scholars have endorsed the Internet of Things (IoT) as an effective transformative tool for shifting traditional farming to smart farming, relatively little study has addressed the enabling role of smart agriculture in achieving sustainable agriculture and green climate. Researchers are more focused on technological invention and model introduction rather than discussing societal or global development goals. Sustainable development goals (SDGs) designed by United Nations (UN), therefore demand discussions as SDGs targets have a closer implication of technology. To fill this gap, in this study a model of smart agriculture is developed and centring the model we investigated how the model addresses SDGs targets. The investigation suggests that smart agriculture supports targets mentioned in Goal 6, 7, 8, 9, 11 and 12 of SDG. This research is very important, both for developing and developed nations since most of the nations are moving more towards industrialization and aiming to achieve the SDG goals This research is expected to provide a path to the IT practitioners, governments and developing agencies on how technological intervention can provide a more sustainable agricultural world.
Closing the Performance Gap with Modern C++
Venue: arXiv cs.DC 2022
Year: 2022

On the way to Exascale, programmers face the increasing challenge of having to support multiple hardware architectures from the same code base. At the same time, portability of code and performance are increasingly difficult to achieve as hardware architectures are becoming more and more diverse. Today's heterogeneous systems often include two or more completely distinct and incompatible hardware execution models, such as GPGPU's, SIMD vector units, and general purpose cores which conventionally have to be programmed using separate tool chains representing non-overlapping programming models. The recent revival of interest in the industry and the wider community for the C++ language has spurred a remarkable amount of standardization proposals and technical specifications in the arena of concurrency and parallelism. This recently includes an increasing amount of discussion around the need for a uniform, higher-level abstraction and programming model for parallelism in the C++ standard targeting heterogeneous and distributed computing. Such an abstraction should perfectly blend with existing, already standardized language and library features, but should also be generic enough to support future hardware developments. In this paper, we present the results from developing such a higher-level programming abstraction for parallelism in C++ which aims at enabling code and performance portability over a wide range of architectures and for various types of parallelism. We present and compare performance data obtained from running the well-known STREAM benchmark ported to our higher level C++ abstraction with the corresponding results from running it natively. We show that our abstractions enable performance at least as good as the comparable base-line benchmarks while providing a uniform programming API on all compared target architectures.
Language Models are General-Purpose Interfaces
Venue: arXiv cs.CL 2022
Year: 2022

Foundation models have received much attention due to their effectiveness across a broad range of downstream applications. Though there is a big convergence in terms of architecture, most pretrained models are typically still developed for specific tasks or modalities. In this work, we propose to use language models as a general-purpose interface to various foundation models. A collection of pretrained encoders perceive diverse modalities (such as vision, and language), and they dock with a language model that plays the role of a universal task layer. We propose a semi-causal language modeling objective to jointly pretrain the interface and the modular encoders. We subsume the advantages and capabilities from both causal and non-causal modeling, thereby combining the best of two worlds. Specifically, the proposed method not only inherits the capabilities of in-context learning and open-ended generation from causal language modeling, but also is conducive to finetuning because of the bidirectional encoders. More importantly, our approach seamlessly unlocks the combinations of the above capabilities, e.g., enabling in-context learning or instruction following with finetuned encoders. Experimental results across various language-only and vision-language benchmarks show that our model outperforms or is competitive with specialized models on finetuning, zero-shot generalization, and few-shot learning.
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process
Venue: arXiv cs.LG 2022
Year: 2022

In typical scenarios where the Federated Learning (FL) framework applies, it is common for clients to have insufficient training data to produce an accurate model. Thus, models that provide not only point estimations, but also some notion of confidence are beneficial. Gaussian Process (GP) is a powerful Bayesian model that comes with naturally well-calibrated variance estimations. However, it is challenging to learn a stand-alone global GP since merging local kernels leads to privacy leakage. To preserve privacy, previous works that consider federated GPs avoid learning a global model by focusing on the personalized setting or learning an ensemble of local models. We present Federated Bayesian Neural Regression (FedBNR), an algorithm that learns a scalable stand-alone global federated GP that respects clients' privacy. We incorporate deep kernel learning and random features for scalability by defining a unifying random kernel. We show this random kernel can recover any stationary kernel and many non-stationary kernels. We then derive a principled approach of learning a global predictive model as if all client data is centralized. We also learn global kernels with knowledge distillation methods for non-identically and independently distributed (non-i.i.d.) clients. Experiments are conducted on real-world regression datasets and show statistically significant improvements compared to other federated GP models.
EnergyMatch: Energy-based Pseudo-Labeling for Semi-Supervised Learning
Venue: arXiv cs.CV 2022
Year: 2022

Recent state-of-the-art methods in semi-supervised learning (SSL) combine consistency regularization with confidence-based pseudo-labeling. To obtain high-quality pseudo-labels, a high confidence threshold is typically adopted. However, it has been shown that softmax-based confidence scores in deep networks can be arbitrarily high for samples far from the training data, and thus, the pseudo-labels for even high-confidence unlabeled samples may still be unreliable. In this work, we present a new perspective of pseudo-labeling: instead of relying on model confidence, we instead measure whether an unlabeled sample is likely to be "in-distribution"; i.e., close to the current training data. To classify whether an unlabeled sample is "in-distribution" or "out-of-distribution", we adopt the energy score from out-of-distribution detection literature. As training progresses and more unlabeled samples become in-distribution and contribute to training, the combined labeled and pseudo-labeled data can better approximate the true distribution to improve the model. Experiments demonstrate that our energy-based pseudo-labeling method, albeit conceptually simple, significantly outperforms confidence-based methods on imbalanced SSL benchmarks, and achieves competitive performance on class-balanced data. For example, it produces a 4-6% absolute accuracy improvement on CIFAR10-LT when the imbalance ratio is higher than 50. When combined with state-of-the-art long-tailed SSL methods, further improvements are attained.
Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

The task of unsupervised semantic segmentation aims to cluster pixels into semantically meaningful groups. Specifically, pixels assigned to the same cluster should share high-level semantic properties like their object or part category. This paper presents MaskDistill: a novel framework for unsupervised semantic segmentation based on three key ideas. First, we advocate a data-driven strategy to generate object masks that serve as a pixel grouping prior for semantic segmentation. This approach omits handcrafted priors, which are often designed for specific scene compositions and limit the applicability of competing frameworks. Second, MaskDistill clusters the object masks to obtain pseudo-ground-truth for training an initial object segmentation model. Third, we leverage this model to filter out low-quality object masks. This strategy mitigates the noise in our pixel grouping prior and results in a clean collection of masks which we use to train a final segmentation model. By combining these components, we can considerably outperform previous works for unsupervised semantic segmentation on PASCAL (+11% mIoU) and COCO (+4% mask AP50). Interestingly, as opposed to existing approaches, our framework does not latch onto low-level image cues and is not limited to object-centric datasets. The code and models will be made available.
GoAutoBash: Golang-based Multi-Thread Automatic Pull-Execute Framework with GitHub Webhooks And Queuing Strategy
Venue: arXiv cs.NI 2022
Year: 2022

Recently, more and more server tasks are done using full automation, including grading tasks for students in the college courses, integrating tasks for programmers in big projects and server-based transactions, and visualization tasks for researchers in a data-dense topic. Using automation on servers provides a great possibility for reducing the burden on manual tasks. Although server tools like CI/CD for continuous integration and Hexo for automated blog deployment have been developed, they're highly dedicated to certain functionalities and thus lack general usage. In this paper, we introduce a Golang-based automation framework that reacts to the events happening on GitHub in a multi-thread approach. This framework utilizes a queue to arrange the tasks submitted and execute each task with a thread in a preemptive manner. We then use the project GoAutoGrader to illustrate a specific implementation of this framework and its value in implementing high-freedom server applications. As Golang is developing in a rapid way because of its incredible parallel programming efficiency and a super-easy way to learn on the basis of C-like programming languages, we decide to develop this system in Golang.
Compositional Mixture Representations for Vision and Text
Venue: arXiv cs.CV 2022
Year: 2022

Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture representation imposing the compositionality of the text onto the visual domain without having explicit location supervision. By combining the spatial transformer with a representation learning approach we learn to split images into separately encoded patches to associate visual and textual representations in an interpretable manner. On variations of MNIST and CIFAR10, our model is able to perform weakly supervised object detection and demonstrates its ability to extrapolate to unseen combination of objects.
A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative
Venue: arXiv cs.AI 2022
Year: 2022

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful to assess associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases and the simple removal of these cases may introduce severe bias. For these reasons, several multiple imputation algorithms have been proposed to attempt to recover the missing information. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithms works best in a given scenario. Furthermore, the selection of each algorithm parameters and data-related modelling choices are also both crucial and challenging. In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. The experiments presented here show that our approach could effectively highlight the most valid and performant missing-data handling strategy for our case study. Moreover, our methodology allowed us to gain an understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.
A Dilemma for Solomonoff Prediction
Venue: arXiv cs.AI 2022
Year: 2022

The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of Universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because computable approximations to Solomonoff prediction do not always converge.
Explainable Mixed Data Representation and Lossless Visualization Toolkit for Knowledge Discovery
Venue: arXiv cs.LG 2022
Year: 2022

Developing Machine Learning (ML) algorithms for heterogeneous/mixed data is a longstanding problem. Many ML algorithms are not applicable to mixed data, which include numeric and non-numeric data, text, graphs and so on to generate interpretable models. Another longstanding problem is developing algorithms for lossless visualization of multidimensional mixed data. The further progress in ML heavily depends on success interpretable ML algorithms for mixed data and lossless interpretable visualization of multidimensional data. The later allows developing interpretable ML models using visual knowledge discovery by end-users, who can bring valuable domain knowledge which is absent in the training data. The challenges for mixed data include: (1) generating numeric coding schemes for non-numeric attributes for numeric ML algorithms to provide accurate and interpretable ML models, (2) generating methods for lossless visualization of n-D non-numeric data and visual rule discovery in these visualizations. This paper presents a classification of mixed data types, analyzes their importance for ML and present the developed experimental toolkit to deal with mixed data. It combines the Data Types Editor, VisCanvas data visualization and rule discovery system which is available on GitHub.